clusboot {ClusBoot}  R Documentation 
Performs bootstrap on a cluster analysis output
Description
Performs bootstrap on a cluster analysis output
Usage
clusboot(datmat, B = 1000, clustering.func = complete.linkage, ...)
Arguments
datmat 
a data matrix or distance object which will be the input to the clustering function 
B 
number of bootstrap replicates 
clustering.func 
the function which will perform the clustering and output a vector of cluster memberships 
... 
more arguments to be passed to the clustering function 
Details
Any R function performing cluster analysis can be specified in clustering.func
although a wrapper function is
typically needed to isolate only the vector output of cluster memberships. See ?complete.linkage
as an example.
Should users perfer to use alternative resamling schemes, other than the bootstrap, Hennig (2007) discuss a variety
of options which could be accessed by specifying clustering.func = fpc.clusterboot
. In addition, the sampling
method is specified in the argument bootmethod
and additional arguments for the function clusterboot
in the
package fpc
must be given. Note that only the resampling facilities of clusterboot
is utilised while the
computation of proportions and silhouette widths remain unchanged. The output object of class clusboot
will remain unchanged as only the resampling section of clusterboot
is used.
Value
an object of class clusboot
which is a list with the following components:
proportions 
matrix of size nxn with cell ij containing the proportion of bootstrap replicates in which object i and object j clustered together. 
clustering 
a vector of length n containing the cluster membership of the n input objects. 
sil 
a vector of length the number of clusters containing the bootstrapsilhouette values for the clusters. 
indv.sil 
a vector of length n containing the bootstrapsilhouette values for the individual objects. 
sil.order 
a vector of length n containing the ordering of the n objects used by the functions

ave.sil.width 
the overall stability of the clustering solution, obtained by averaging over the individual object bootstrapsilhouette values. 
References
Hennig, C., 2007. Clusterwise assessment of cluster stability. Computational Statistics & Data Analysis, 52(1), pp.258271.
Examples
clusboot (scale(case.study.psychiatrist), B=100, k=6, clustering.func=complete.linkage)
library(fpc)
clusboot (scale(case.study.psychiatrist), B=100, k=6, clustering.func=fpc.clusterboot,
clustermethod=hclustCBI, method="complete", bootmethod="subset", subtuning=10)